Three-dimensional vector estimation for subcomponents of space object imagery

We describe a model-based image analysis system that automatically estimates the 3-D orientation vectors of satellites and their subcomponents by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database and pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index-hashing method is introduced. An affine point-matching method is also introduced for improving system performance on a wide variety of satellite shapes. We present recent results, which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites.